Professional Data EngineerEnsuring solution qualityMedium
A global media streaming service processes billions of user interaction events (clicks, views, searches) in real-time. They need to monitor their streaming data pipelines for data quality issues, such as missing events, malformed data, or sudden drops in throughput, and be alerted immediately. The solution must provide real-time visibility into data health and trigger notifications to the data engineering team. Which combination of Google Cloud services is best suited to implement real-time data quality monitoring and alerting for this streaming pipeline?
- ACloud Monitoring with custom metrics and Cloud Logging with log-based alerts
- BBigQuery with scheduled queries and Cloud Scheduler
- CData Catalog and Dataplex
- DCloud Data Loss Prevention (DLP) and Security Command Center
Show answer & explanationAnswer & explanation
Correct answer: A. Cloud Monitoring with custom metrics and Cloud Logging with log-based alerts
Cloud Monitoring with custom metrics allows you to define specific data quality indicators (e.g., event count, error rates) and track them in real-time. Cloud Logging can capture detailed logs from the streaming pipeline, and log-based alerts can trigger notifications when specific patterns (e.g., malformed data errors) or thresholds are met, providing immediate visibility and alerting for data quality issues.
Why the other options are wrong
- B. BigQuery with scheduled queries is for batch analysis and reporting, not real-time monitoring and immediate alerting on streaming data quality.
- C. Data Catalog provides metadata management, and Dataplex helps manage data lakes, but they don't offer real-time operational monitoring and alerting capabilities for streaming data quality.
- D. Cloud DLP focuses on sensitive data discovery and protection, and Security Command Center is for security posture management; neither is designed for real-time data quality monitoring.
GCP Monitoring for Streaming Data Quality
Leveraging Cloud Monitoring and Cloud Logging to collect metrics and logs from streaming pipelines to detect and alert on data quality issues in real-time.
- Custom metrics track key performance indicators (KPIs) like event counts, latencies, error rates.
- Log-based metrics and alerts can detect specific data patterns or errors in logs.
- Integrates with Pub/Sub, Dataflow, and other streaming services.
Memory trick: Monitor Metrics, Log Lapses.